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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Telcomatics Jurnal UNITEK Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Sistem Informasi Mahakarya (JSIM) Journal of Information System and Education Development JAR'S (Journal of Advanced Research in Informatics) International Journal of Applied Mathematics and Computing. International Journal of Mechanical, Industrial and Control Systems Engineering JIeTri : Journal of Industrial Engineering Tridinanti Paidagogia Green Engineering: Journal of Engineering and Applied Science JBIAM Global Science: Journal of Information Technology and Computer Science Big Data Analytics and Data Science Technema: Journal of Intelligent Engineering and Computing
Trisnawan, Ahmad Budi
Universitas Mahakarya Asia
Agriculture, Biological Sciences & Forestry Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Integrasi Big Data dan Sistem Informatika Manufaktur dalam Prediksi Permintaan Produksi Ahmad Budi Trisnawan Wawan
JURNAL TEKNIK DAN SISTEM INDUSTRI Vol 3 No 01 (2025): Edisi Januari-Juni
Publisher : PROGRAM STUDI TEKNIK INDUSTRI UNIVERSITAS TRIDINANTI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dalam era industri 4.0, kemampuan untuk mengelola dan menganalisis data dalam skala besar menjadi kebutuhan utama bagi sektor manufaktur yang ingin meningkatkan efisiensi dan daya saing. Penelitian ini mengkaji integrasi teknologi Big Data dengan sistem informatika manufaktur untuk mendukung proses prediksi permintaan produksi secara lebih akurat dan adaptif. Sistem informatika manufaktur konvensional sering kali terbatas dalam mengolah data real-time yang berasal dari berbagai sumber seperti histori penjualan, tren pasar, kondisi cuaca, hingga perilaku konsumen digital. Dengan memanfaatkan arsitektur Big Data, data dalam volume besar dapat dikumpulkan, disimpan, dan dianalisis secara cepat menggunakan algoritma prediktif berbasis machine learning, seperti Random Forest dan Long Short-Term Memory (LSTM). Studi ini menunjukkan bahwa integrasi ini tidak hanya meningkatkan akurasi prediksi permintaan, tetapi juga membantu pengambilan keputusan dalam penjadwalan produksi, pengelolaan persediaan, dan distribusi. Hasil evaluasi model prediktif memperlihatkan peningkatan akurasi hingga lebih dari 90% dibandingkan metode tradisional. Implementasi sistem ini juga memberikan dampak signifikan terhadap pengurangan biaya operasional dan peningkatan responsivitas terhadap perubahan pasar. Temuan ini mempertegas pentingnya transformasi digital berbasis data dalam mendukung sistem manufaktur yang cerdas dan adaptif.
Energy Aware Software Architecture Optimization Using Real Time Analytics and Self Adaptive Control in Intelligent Computing Systems Ardy Wicaksono; Mursalim Mursalim; Arif Tri Widiyatmoko; Deny Prasetyo; Ahmad Budi Trisnawan; Yanuar Wicaksono
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.195

Abstract

The increasing demand for intelligent computing systems, including cloud computing, artificial intelligence (AI), and the Internet of Things (IoT), has resulted in a significant rise in energy consumption, which poses both environmental and economic challenges. The high computational power required by these systems, coupled with the continuous operation of data centers and connected devices, has led to inefficiencies in energy usage. This paper explores the integration of real time analytics and self adaptive control mechanisms to optimize energy consumption in intelligent systems. By employing advanced software tools for real time monitoring, dynamic adjustments based on workload conditions, and adaptive algorithms for energy optimization, significant reductions in power usage were achieved without compromising system performance. The optimized architecture dynamically adjusts system parameters such as processor frequency, task scheduling, and voltage to ensure efficient energy consumption during varying operational demands. The results show a 24% reduction in energy usage during low demand periods, demonstrating the potential of real time energy management strategies. The study also compares the optimized architecture with conventional static systems, highlighting the benefits of dynamic energy management, including improved performance balance, reduced environmental impact, and lower operational costs. These findings suggest that the integration of energy efficient practices in software design, particularly through real time analytics and self adaptive mechanisms, offers a sustainable solution for modern computing systems. Future research could focus on improving self adaptive systems, incorporating renewable energy sources, and expanding the approach to other intelligent systems, such as autonomous vehicles or large scale smart grids. The practical applications of this research are vast, particularly in large scale applications such as data centers and cloud computing, where energy efficiency is critical for sustainability.
Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery Suyahman Suyahman; Deny Prasetyo; Ahmad Budi Trisnawan; Ardy Wicaksono; Muhamad Furqon
International Journal of Mechanical, Industrial and Control Systems Engineering Vol. 1 No. 1 (2024): March: IJMICSE: International Journal of Mechanical, Industrial and Control Sys
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijmicse.v1i1.402

Abstract

Predictive maintenance (PdM) plays a crucial role in modern industrial systems by minimizing downtime, reducing maintenance costs, and optimizing asset performance. However, many predictive models operate as “black box” systems, limiting transparency and making it difficult for operators to interpret their outputs. This study aims to integrate Explainable Artificial Intelligence (XAI) techniques with Remaining Useful Life (RUL) prediction models to improve both accuracy and interpretability. Various machine learning and deep learning approaches, including Support Vector Machines (SVM), Random Forest (RF), XGBoost, Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), are employed to predict RUL using real-time sensor data from rotating machinery. XAI methods such as SHAP, LIME, and attention mechanisms are applied to provide human-understandable explanations of model predictions. The models are evaluated based on accuracy, Root Mean Square Error (RMSE), and interpretability scores. The results show that XAI-enhanced models outperform traditional approaches in predictive performance while offering greater transparency. These explanations help maintenance engineers better understand the factors influencing predictions, thereby improving decision-making and trust in the system. Nevertheless, the integration of XAI introduces additional computational complexity, which may pose challenges for large-scale industrial implementation. Overall, this study highlights the potential of combining XAI with RUL prediction to develop more reliable, transparent, and effective predictive maintenance solutions.
Adaptive Cyber Secure Software Engineering Practices for Big Data Platforms With Dynamic Access Control and Differential Privacy Mechanisms Ahmad Budi Trisnawan; Priyo Wibowo
Big Data Analytics and Data Science Vol. 1 No. 1 (2026): March: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i1.24

Abstract

Big data platforms face significant challenges related to cybersecurity and privacy due to the vast volume, variety, and velocity of data they manage. Traditional static security measures often fail to address the dynamic and complex nature of big data environments. This research proposes an adaptive cybersecurity framework that integrates dynamic access control and differential privacy mechanisms to enhance both the security and privacy of big data platforms. The dynamic access control mechanism continuously adjusts access permissions in real-time based on changing risk and trust levels, ensuring that sensitive data remains secure even as user roles and data flows evolve. The differential privacy mechanism adds noise to data, preserving individual privacy while allowing for meaningful data analysis. Through simulations and case studies, the framework was evaluated in various real-world environments, including healthcare, IoT, and finance, where it demonstrated scalability, efficiency, and robust security performance. The results showed that the proposed framework significantly reduced unauthorized access attempts and maintained data privacy, while still enabling effective data analysis. Although there were some challenges regarding performance overhead, particularly in resource-constrained environments, the framework remained effective in large-scale systems. The findings highlight the importance of adaptive security practices in big data environments and suggest that future research should focus on refining dynamic security mechanisms and applying differential privacy in diverse real-world scenarios. These advancements are essential for ensuring that big data platforms can handle evolving cyber threats without compromising data utility or privacy.
Explainable Artificial Intelligence for Engineering Decision Support Systems Rezi Munizar; Ahmad Budi Trisnawan; Maiza Fikri; Tarma Tarma; Eko Sutrisno
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Explainable Artificial Intelligence (XAI) has become increasingly important in Engineering Decision Support Systems because growing algorithmic complexity often reduces transparency, accountability, and practitioner confidence despite substantial improvements in predictive capability. This study develops a conceptual framework that integrates engineering data processing, artificial intelligence inference, explainability mechanisms, and human-centered decision support within a unified multilayer architecture. A non-empirical system design methodology based on design-oriented systems research, architecture-based evaluation, and scenario-driven analytical simulation is employed to examine architectural consistency, interoperability, traceability, and explainability across representative engineering contexts. The analytical results indicate that explanation fidelity, interpretability, transparency, traceability, and modular scalability function as complementary engineering quality attributes that collectively strengthen trustworthy decision support while preserving logical consistency between engineering evidence and computational reasoning. The proposed architecture also demonstrates technology independence and adaptability across heterogeneous engineering domains through explicit modular interactions and standardized information flows. This study contributes an architecture-centered perspective that advances theoretical understanding of explainable engineering intelligence while providing a reproducible conceptual foundation for future empirical implementation, quantitative validation, and standardized evaluation of trustworthy Engineering Decision Support Systems.  
Efektivitas Media Pembelajaran Berbasis Teknologi untuk Siswa SDN Kunciran 3 Ahmad Budi Trisnawan Wawan
Paidagogia: Jurnal Pengajaran dan Pendidikan Vol. 1 No. 3 (2025): July 2025 : PAIDAGOGIA: Jurnal Pengajaran dan Pendidikan
Publisher : Sumule Indonesia Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66964/xj4b5d94

Abstract

This study aims to analyze the effectiveness of technology-based learning media in improving the quality of the teaching and learning process and student learning outcomes at SDN Kunciran 3. In the context of 21st-century education, integrating technology into learning activities is a necessity for creating an interactive, enjoyable, and relevant learning environment. The research method used is a quantitative method with a quasi-experimental approach, involving two groups of fifth-grade students: an experimental group that uses technology-based learning media such as interactive videos, educational applications, and digital quizzes; and a control group that uses conventional methods in the form of lectures and textbooks. Data were collected through pre-tests and post-tests to measure improvements in learning outcomes, as well as questionnaires to assess student motivation and responses to the learning media used. The results showed that students in the experimental group experienced a significant increase in learning outcomes compared to the control group, with an increase in learning motivation that was also higher. This study concludes that the use of technology-based learning media has a positive impact on learning effectiveness, especially in improving understanding of the material, active student involvement, and interest in learning. Therefore, technology integration in learning at the elementary school level is highly recommended to support a more modern and adaptive educational transformation.
Sustainable Precision Agriculture Irrigation System Using Edge Computing and Renewable Energy Integration for Water Conservation and Climate Adaptation Agus Wantoro; Ferly Ardhy; Fahlul Rizki; Ahmad Budi Trisnawan; Yulaikha Mar’atullatifah; Rachmat Setiabudi
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.288

Abstract

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.
Adaptive Edge-AI Framework for Real-Time Cyber-Physical Systems in Smart Cities with Resource-Constrained IoT Devices Benny Martha Dinata; Ahmad Budi Trisnawan; Eram Abbasi
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 2 (2025): June: Global Science: Journal of Information Technology and Computer Science
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i2.170

Abstract

This research focuses on the development and evaluation of an Adaptive Edge-AI framework designed to optimize real-time data processing and decision-making in resource-constrained environments, specifically within smart city infrastructures. The primary problem addressed is the challenge of minimizing latency, reducing energy consumption, and ensuring the reliability of Cyber-Physical Systems (CPS) when using Internet of Things (IoT) devices. The objective of the study is to assess the effectiveness of this framework in real-world smart city applications such as traffic monitoring, environmental sensing, and smart utilities management. The proposed method integrates lightweight AI models, edge computing, and adaptive resource management techniques, including Federated Learning and Neural Architecture Search, to ensure optimal performance while addressing hardware constraints. The main findings reveal that the framework significantly improves real-time inference speed, reduces energy consumption of IoT devices, and enhances CPS reliability by minimizing communication delays and ensuring continuous system operation despite network disruptions. The application of this framework to smart transportation and urban utilities further demonstrates its potential to optimize city management processes. The study concludes that the Adaptive Edge-AI framework offers a promising solution for smart cities, enhancing operational efficiency, sustainability, and resilience. It is recommended for integration into smart city infrastructures to enable better resource management and decision-making in real-time applications.
Prediksi Harga Bitcoin Menggunakan Support Vector Regression (SVR) Berbasis Particle Swarm Optimization (PSO) Parlika, Rizky; Wahyudi, Wahyudi; Budi Trisnawan, Ahmad; Munazilin, Akhlis; Karunia Farista Ananto, Prasasti
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11583

Abstract

Bitcoin prices exhibit high volatility and complex non-linear patterns, making accurate price predictions challenging in the context of cryptocurrency market research. In this study, an optimized SVR-PSO predictive modeling approach is developed to help predict Bitcoin prices, and its performance is compared against a Grid Search-optimized SVR baseline. The SVR-PSO modeling approach developed in this study uses daily historical Bitcoin records obtained from Yahoo Finance between March 10, 2019, and May 11, 2026. The data consists of open, high, low, close, and volume values as attributes, and the closed value as the target. Data preprocessing consists of chronologically organizing the data, removing blank rows, min-max scaling, and splitting the data into 80:20 for training and testing. PSO is used to identify the optimal SVR parameters C, ε, and γ with the aim of reducing MAPE. The optimal parameters for the SVR model are C = 501, ε = 0.001, and γ = 0.001. On the test dataset, the SVR-PSO model achieved an MAE of 1,521.47, an RMSE of 2,101.70, a MAPE of 1.65%, and an R² of 0.9813, outperforming the SVR-GridSearch baseline (MAE 1,559.87; RMSE 2,128.52; MAPE 1.69%; R² 0.9809). These results suggest that SVR-based Bitcoin price prediction is more accurate when optimized using PSO than using a discrete Grid Search.
Co-Authors Agus Wantoro Akhlis Munazilin, Akhlis Ardy Wicaksono Ardy Wicaksono Arif Tri Widiyatmoko Benny Martha Dinata Deny Prasetyo Deny Prasetyo Dewi Mulyama Dian Afriady Eko Sutrisno Elkin Rilvani Eram Abbasi Erlita Sulistiati Erlita Sulistiati Fahlul Rizki Ferly Ardhy Maiza Fikri Muhamad Furqon Mursalim Mursalim Nur Amalia Parlika, Rizky Prasasti Karunia Farista Ananto Priasnyomo Prima Santoso Priyo Wibowo Rachmat Setiabudi Rahayu, Tri Mulyaningtyas Rezi Munizar Sinatrya, Yuanita Suryadi Suryadi Suyahman Suyahman Syed Asif Ali Tarma TUTI SUSILAWATI Wahyudi Wahyudi Yanuar Wicaksono Yulaikha Mar’atullatifah